Mapping invasive alien plant species with very high spatial resolution and multi-date satellite imagery using object-based and machine learning techniques: A comparative study

Abstract

Invasive alien plant species (IAPS) have negative impacts on ecosystems, including the loss of biodiversity and the alteration of ecosystem functions. The strategy for mitigating these impacts requires knowledge of these species’ spatial distribution and level of infestation. In situ inventories or aerial photo interpretation can be used to collect these data but they are labor-intensive, time-consuming, and incomplete, especially when dealing with large or inaccessible areas. Remote sensing may be an effective method of mapping IAPS for a better management strategy. Several studies using remote sensing to map IAPS have focused on single species detection and were conducted in relatively homogeneous natural environments, while other common, more heterogeneous environments, such as urban areas, are often invaded by multiple IAPS, posing management challenges. The main objective of this study was to develop a mapping method for three major IAPS observed in the urban agglomeration of Quebec City (Canada), namely Japanese knotweed (Fallopia japonica); giant hogweed (Heracleum mantegazzianum); and phragmites (Phragmites australis). Mono-date and multi-date classification approaches were used with WorldView-3 and SPOT-7 satellite imagery, acquired in the summer of 2020 and in the autumn of 2019, respectively. To estimate presence probability, object-based image analysis (OBIA) and nonparametric classifiers such as Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were used. Overall, multi-date classification using WorldView-3 and SPOT-7 images produced the best results, with a Kappa coefficient of 0.85 and an overall accuracy of 91% using RF. For XGBoost, the Kappa coefficient was 0.81 with an overall accuracy of 89%, whereas the Kappa coefficient and overall accuracy were 0.80 and 88% for SVM classifier, respectively. Individual class performances based on F1-score revealed that Japanese knotweed had the highest maximum value (0.95), followed by giant hogweed (0.91), and phragmites (0.87). These results confirmed the potential of remote sensing to accurately map and simultaneously monitor the main IAPS in a heterogeneous urban environment using a multi-date approach. Although the approach is limited by image and reference data availability, it provides new tools to managers for IAPS invasion control.

Keywords

• invasive alien plant species
• remote sensing
• multi-date
• satellite imagery
• obia
• machine learning